Occupancy sensing systems are used in a wide variety of applications. Typically, they provide input to other applications, such as a Building Automation System. As a result, it is desired that these occupancy sensing systems are relatively cheap, i.e. below the cost reductions that are achievable through applications that make use of the 2D or 3D occupancy knowledge, such as a smart building system that switches off lights for energy saving depending on the location and activity of people in the room.
One dimensional (1D) occupancy sensing systems are able to detect and observe the behaviour of a person or crowd, but not the fine details of their movements. Such one dimensional occupancy systems can generate numerical or statistical information but are not able to produce a 2D or 3D view on the occupancy of a space by people and objects. An example system that uses 1D occupancy sensors is described in the article “Buzz: Measuring and Visualizing Conference Crowds” from the authors Christopher Wren, Yuri Ivanov and Darren Leigh.
In order to be able to produce a 2D occupancy map, various 2D occupancy sensing systems have been proposed in literature: these existing 2D occupancy sensing systems are either based on cameras, passive infrared or PIR sensors, pressure sensitive carpets, or active beacons based on radar, ultrasound or radio technology. A combination of two or more of the foregoing techniques is also possible.
Camera based systems for 2D occupancy sensing, such as for instance the one described in U.S. Pat. No. 7,450,735 entitled “Tracking Across Multiple Cameras with Disjoint Views”, rely on a single top-view camera or multiple cameras (side-view, top-view or a combination of both) in order to generate images that can be processed to establish an occupancy map of the room. These cameras are usually visible within the room and also the required wiring, e.g. for powering the cameras, has infrastructural and aesthetic implications. The cameras have a relatively high power consumption as a result of which it is difficult to operate them on battery power for the purpose of occupancy sensing. The cameras in other words need to be connected to power wiring or power over Ethernet wiring. Regular cameras produce detailed, high resolution images which further may pose these systems at risk of privacy breach when used for occupancy sensing. At last, the images generated by real-time still or video cameras require high bit rates and expensive image processing in order to turn the images into a 2D occupancy map. A single top-view camera reduces the costs and processing requirements in comparison with multiple cameras, but still suffers from excessive power consumption, the need for wiring, and the risk for privacy breaches. Moreover, a single top-view camera may loose accuracy when used in large rooms or rooms with a more complex geometry.
Existing solutions based on pressure sensors integrated in carpets, seats, flooring, etc. have as main disadvantage their intrusive nature in the interior of the room. Occupancy sensing systems based thereon require expensive alterations to existing infrastructure in case of renovation, or limit the choice of flooring materials significantly in case of new construction. In addition, these pressure sensor based occupancy sensing systems involve wiring for power and data because their power consumption does not allow battery-based operation.
State of the art occupancy sensing systems based on active sensors such as radar, ultrasound or radiofrequency (RF) based beacons typically need extensive noise cancellation and excessive processing because direction of arrival methods have to be applied. The position measurements obtained with these technologies are not as accurate as can be achieved with camera-based systems. Their power consumption is relatively high—they are active sensors—whereas their angular resolution is rather limited. The article “Positioning with Independent Ultrasonic Beacons” from the authors Michael McCarthy and Henk Muller for instance describes a system for positioning sensing based on ultrasound technology. A system based on RF technology is described in the article “RADAR: An In-Building RF-Based User Location and Tracking System” from the authors Paramvir Bahl and Venkata N. Padmanabhan.
PIR or passive infrared sensors generate binary output signals: motion or no motion. Their angular resolution is limited and as a result of the binary output, also the accuracy obtainable with PIR technology is limited. As a result, a large number of PIR sensors would be needed in order to obtain a reliable and accurate system for occupancy sensing. Occupancy systems based on PIR sensors are for instance described in U.S. Pat. No. 7,486,193 entitled “Occupancy Sensor Network”.
It is an objective of the present invention to disclose a system and method for occupancy sensing that overcome the shortcomings of the above cited existing systems. In particular, it is an objective to disclose a system for occupancy sensing that is not intrusive and allows accurate sensing and cost-efficient generation of a 2D or 3D occupancy map with low energy consumption and without the risk for privacy breach when used in enterprises, elderly homes, etc.